The present invention relates generally to data analysis and, more particularly, to methods, systems, and computer program products for identifying cyclical behaviors.
In monitoring a large number of metrics that do not fit a steady state pattern, but instead fit a cyclical state, active monitoring may not effectively rely on existing techniques such as boundary alerting. Boundary alerting involves establishing pre-defined static values and using the pre-defined static values to establish a baseline of acceptable behavior. This may be implemented by defining alert rules specific to each metric after first determining what the norm is for the metric. However, such techniques are not practical with large amounts of data in which a method of establishing and alerting on deviation of trends is desired, as this would require very complex calculations and may introduce inaccuracies. As a result, a graphic display is often used with human interpretation. However, with considering large amounts of data, this can be a cumbersome and error prone approach.
What is needed, therefore, is way to efficiently identify and understand cyclical behaviors.